import os
from collections.abc import Generator

import pytest

from core.model_runtime.entities.llm_entities import LLMResult, LLMResultChunk, LLMResultChunkDelta
from core.model_runtime.entities.message_entities import (
    AssistantPromptMessage,
    ImagePromptMessageContent,
    PromptMessageTool,
    SystemPromptMessage,
    TextPromptMessageContent,
    UserPromptMessage,
)
from core.model_runtime.entities.model_entities import AIModelEntity, ModelType
from core.model_runtime.errors.validate import CredentialsValidateFailedError
from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
from core.model_runtime.model_providers.openai.llm.llm import OpenAILargeLanguageModel

"""FOR MOCK FIXTURES, DO NOT REMOVE"""
from tests.integration_tests.model_runtime.__mock.openai import setup_openai_mock


def test_predefined_models():
    model = OpenAILargeLanguageModel()
    model_schemas = model.predefined_models()

    assert len(model_schemas) >= 1
    assert isinstance(model_schemas[0], AIModelEntity)

@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_validate_credentials_for_chat_model(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(
            model='gpt-3.5-turbo',
            credentials={
                'openai_api_key': 'invalid_key'
            }
        )

    model.validate_credentials(
        model='gpt-3.5-turbo',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        }
    )

@pytest.mark.parametrize('setup_openai_mock', [['completion']], indirect=True)
def test_validate_credentials_for_completion_model(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    with pytest.raises(CredentialsValidateFailedError):
        model.validate_credentials(
            model='text-davinci-003',
            credentials={
                'openai_api_key': 'invalid_key'
            }
        )

    model.validate_credentials(
        model='text-davinci-003',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        }
    )

@pytest.mark.parametrize('setup_openai_mock', [['completion']], indirect=True)
def test_invoke_completion_model(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    result = model.invoke(
        model='gpt-3.5-turbo-instruct',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY'),
            'openai_api_base': 'https://api.openai.com'
        },
        prompt_messages=[
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens': 1
        },
        stream=False,
        user="abc-123"
    )

    assert isinstance(result, LLMResult)
    assert len(result.message.content) > 0
    assert model._num_tokens_from_string('gpt-3.5-turbo-instruct', result.message.content) == 1

@pytest.mark.parametrize('setup_openai_mock', [['completion']], indirect=True)
def test_invoke_stream_completion_model(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    result = model.invoke(
        model='gpt-3.5-turbo-instruct',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY'),
            'openai_organization': os.environ.get('OPENAI_ORGANIZATION'),
        },
        prompt_messages=[
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens': 100
        },
        stream=True,
        user="abc-123"
    )

    assert isinstance(result, Generator)

    for chunk in result:
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)
        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True

@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_invoke_chat_model(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    result = model.invoke(
        model='gpt-3.5-turbo',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'top_p': 1.0,
            'presence_penalty': 0.0,
            'frequency_penalty': 0.0,
            'max_tokens': 10
        },
        stop=['How'],
        stream=False,
        user="abc-123"
    )

    assert isinstance(result, LLMResult)
    assert len(result.message.content) > 0

    for chunk in model._llm_result_to_stream(result):
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)
        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True

@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_invoke_chat_model_with_vision(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    result = model.invoke(
        model='gpt-4-vision-preview',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content=[
                    TextPromptMessageContent(
                        data='Hello World!',
                    ),
                    ImagePromptMessageContent(
                        data=''
                    )
                ]
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens': 100
        },
        stream=False,
        user="abc-123"
    )

    assert isinstance(result, LLMResult)
    assert len(result.message.content) > 0

@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_invoke_chat_model_with_tools(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    result = model.invoke(
        model='gpt-3.5-turbo',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content="what's the weather today in London?",
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens': 100
        },
        tools=[
            PromptMessageTool(
                name='get_weather',
                description='Determine weather in my location',
                parameters={
                    "type": "object",
                    "properties": {
                      "location": {
                        "type": "string",
                        "description": "The city and state e.g. San Francisco, CA"
                      },
                      "unit": {
                        "type": "string",
                        "enum": [
                          "c",
                          "f"
                        ]
                      }
                    },
                    "required": [
                      "location"
                    ]
                  }
            ),
            PromptMessageTool(
                name='get_stock_price',
                description='Get the current stock price',
                parameters={
                    "type": "object",
                    "properties": {
                      "symbol": {
                        "type": "string",
                        "description": "The stock symbol"
                      }
                    },
                    "required": [
                      "symbol"
                    ]
                  }
            )
        ],
        stream=False,
        user="abc-123"
    )

    assert isinstance(result, LLMResult)
    assert isinstance(result.message, AssistantPromptMessage)
    assert len(result.message.tool_calls) > 0

@pytest.mark.parametrize('setup_openai_mock', [['chat']], indirect=True)
def test_invoke_stream_chat_model(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    result = model.invoke(
        model='gpt-3.5-turbo',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens': 100
        },
        stream=True,
        user="abc-123"
    )

    assert isinstance(result, Generator)

    for chunk in result:
        assert isinstance(chunk, LLMResultChunk)
        assert isinstance(chunk.delta, LLMResultChunkDelta)
        assert isinstance(chunk.delta.message, AssistantPromptMessage)
        assert len(chunk.delta.message.content) > 0 if chunk.delta.finish_reason is None else True
        if chunk.delta.finish_reason is not None:
            assert chunk.delta.usage is not None
            assert chunk.delta.usage.completion_tokens > 0


def test_get_num_tokens():
    model = OpenAILargeLanguageModel()

    num_tokens = model.get_num_tokens(
        model='gpt-3.5-turbo-instruct',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            UserPromptMessage(
                content='Hello World!'
            )
        ]
    )

    assert num_tokens == 3

    num_tokens = model.get_num_tokens(
        model='gpt-3.5-turbo',
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        tools=[
            PromptMessageTool(
                name='get_weather',
                description='Determine weather in my location',
                parameters={
                    "type": "object",
                    "properties": {
                      "location": {
                        "type": "string",
                        "description": "The city and state e.g. San Francisco, CA"
                      },
                      "unit": {
                        "type": "string",
                        "enum": [
                          "c",
                          "f"
                        ]
                      }
                    },
                    "required": [
                      "location"
                    ]
                }
            ),
        ]
    )

    assert num_tokens == 72

@pytest.mark.parametrize('setup_openai_mock', [['chat', 'remote']], indirect=True)
def test_fine_tuned_models(setup_openai_mock):
    model = OpenAILargeLanguageModel()

    remote_models = model.remote_models(credentials={
        'openai_api_key': os.environ.get('OPENAI_API_KEY')
    })

    if not remote_models:
        assert isinstance(remote_models, list)
    else:
        assert isinstance(remote_models[0], AIModelEntity)

    for llm_model in remote_models:
        if llm_model.model_type == ModelType.LLM:
            break

    assert isinstance(llm_model, AIModelEntity)

    # test invoke
    result = model.invoke(
        model=llm_model.model,
        credentials={
            'openai_api_key': os.environ.get('OPENAI_API_KEY')
        },
        prompt_messages=[
            SystemPromptMessage(
                content='You are a helpful AI assistant.',
            ),
            UserPromptMessage(
                content='Hello World!'
            )
        ],
        model_parameters={
            'temperature': 0.0,
            'max_tokens': 100
        },
        stream=False,
        user="abc-123"
    )

    assert isinstance(result, LLMResult)

def test__get_num_tokens_by_gpt2():
    model = OpenAILargeLanguageModel()
    num_tokens = model._get_num_tokens_by_gpt2('Hello World!')

    assert num_tokens == 3
